Porting the galaxy system to Mandarin Chinese

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.Includes bibliographical references (leaves 83-86).by Chao Wang.M.S

DS-UCAT: Sistema de diálogo multimodal y multilingüe para un entorno educativo

Actas de las IV Jornadas de Tecnología del Habla (JTH 2006)En este artículo presentamos un sistema de diálogo multimodal y multilingüe que estamos desarrollando para proporcionar asistencia a estudiantes y profesores en algunas de sus actividades habituales en un entorno educativo, p. e. en una Facultad de una Universidad. Tenemos previsto que además de interactuar con el usuario, el sistema pueda interactuar con el entorno en que éste se encuentra en un momento dado, el cual puede cambiar a lo largo de una interacción conforme el usuario se mueve dentro del centro educativo. El artículo describe la arquitectura del sistema, muestra cómo se realiza la interacción con la versión actual del mismo, y comenta cómo tenemos previsto utilizar técnicas de inteligencia ambiental para mejorar su funcionamiento.Este trabajo ha sido financiado por el Ministerio de Ciencia y Tecnología, mediante el proyecto TIN2004-03140 Ubiquitous Collaborative Training

Bringing Statistical Methodologies for Enterprise Integration of Conversational Agents

Proceedings of: 9th International Conference on Practical Applications of Agents and Multiagent Systems (PAAMS 11). Salamanca, 6-8 April, 2011In this paper we present a methodology to develop commercial conversational agents that avoids the effort of manually defining the dialog strategy for the dialog management module. Our corpus-based methodology is based on selecting the next system answer by means of a classification process in which the complete dialog history is considered. This way, system developers can employ standards like VoiceXML to simply define system prompts and the associated grammars to recognize the users responses to the prompt, and the statistical dialog model automatically selects the next system prompt.We have applied this methodology for the development of an academic conversational agent.Funded by projects CICYT TIN2008-06742-C02-02/TSI, CICYT TEC 2008-06732-C02-02/TEC, CAM CONTEXTS (S2009/TIC-1485), and DPS2008-07029- C02-02.Publicad

Open Microphone Speech Understanding: Correct Discrimination Of In Domain Speech

An ideal spoken dialogue system listens continually and determines which utterances were spoken to it, understands them and responds appropriately while ignoring the rest This paper outlines a simple method for achieving this goal which involves trading a slightly higher false rejection rate of in domain utterances for a higher correct rejection rate of Out of Domain (OOD) utterances. The system recognizes semantic entities specified by a unification grammar which is specialized by Explanation Based Learning (EBL). so that it only uses rules which are seen in the training data. The resulting grammar has probabilities assigned to each construct so that overgeneralizations are not a problem. The resulting system only recognizes utterances which reduce to a valid logical form which has meaning for the system and rejects the rest. A class N-gram grammar has been trained on the same training data. This system gives good recognition performance and offers good Out of Domain discrimination when combined with the semantic analysis. The resulting systems were tested on a Space Station Robot Dialogue Speech Database and a subset of the OGI conversational speech database. Both systems run in real time on a PC laptop and the present performance allows continuous listening with an acceptably low false acceptance rate. This type of open microphone system has been used in the Clarissa procedure reading and navigation spoken dialogue system which is being tested on the International Space Station

Processing and fusioning multiple heterogeneous information sources in multimodal dialog systems

Proceedings of: 17th International Conference on Information Fusion (FUSION 2014): Salamanca, Spain 7-10 July 2014.Context-aware dialog systems must be able to process very heterogeneous information sources and user input modes. In this paper we propose a method to fuse multimodal inputs into a unified representation. This representation allows the dialog manager of the system to find the best interaction strategy and also select the next system response. We show the applicability of our proposal by means of the implementation of a dialog system that considers spoken, tactile, and also information related to the context of the interaction with its users. Context information is related to the detection of user's intention during the dialog and their emotional state (internal context), and the user's location (external context).This work was supported in part by Projects MINECO TEC2012-37832-C02-01, CICYT TEC2011-28626-C02-02, CAM CONTEXTS (S2009/TIC-1485).Publicad

Multi-Agent System (MAS) Applications in Ambient Intelligence (AmI) Environments

Proceedings of: 8th Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS`10). Salamanca (Spain), 28-30 April 2010Research in context-aware systems has been moving towards reusable and adaptable architectures for managing more advanced human-computer interfaces. Ambient. Intelligence (AmI) investigates computer-based services, which are ubiquitous and based on a variety of objects and devices. Their intelligent and intuitive interfaces act as mediators through which people can interact with the ambient environment. In this paper we present an agent-based architecture which supports the execution of agents in AmI environments. Two case studies are also presented, an airport information system and a railway information system, which uses spoken conversational agents to respond to the user's requests using the contextual information that includes the location information of the user.This work has been partially supported by CICYT TIN2008-06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC, SINPROB, CAM MADRINET S-0505/TIC/0255 and DPS2008-07029-C02-02Publicad

On the Development of Adaptive and User-Centred Interactive Multimodal Interfaces

Multimodal systems have attained increased attention in recent years, which has made possible important improvements in the technologies for recognition, processing, and generation of multimodal information. However, there are still many issues related to multimodality which are not clear, for example, the principles that make it possible to resemble human-human multimodal communication. This chapter focuses on some of the most important challenges that researchers have recently envisioned for future multimodal interfaces. It also describes current efforts to develop intelligent, adaptive, proactive, portable and affective multimodal interfaces

An automatic dialog simulation technique to develop and evaluate interactive conversational agents

During recent years, conversational agents have become a solution to provide straightforward and more natural ways of retrieving information in the digital domain. In this article, we present an agent-based dialog simulation technique for learning new dialog strategies and evaluating conversational agents. Using this technique, the effort necessary to acquire data required to train the dialog model and then explore new dialog strategies is considerably reduced. A set of measures has also been defined to evaluate the dialog strategy that is automatically learned and to compare different dialog corpora. We have applied this technique to explore the space of possible dialog strategies and evaluate the dialogs acquired for a conversational agent that collects monitored data from patients suffering from diabetes. The results of the comparison of these measures for an initial corpus and a corpus acquired using the dialog simulation technique show that the conversational agent reduces the time needed to complete the dialogs and improve their quality, thereby allowing the conversational agent to tackle new situations and generate new coherent answers for the situations already present in an initial model.This work was supported in part by Projects MINECO TEC2012-37832-C02-01, CICYT TEC2011-28626-C02-02, CAM CONTEXTS S2009/TIC-1485Publicad

The Challenge of Spoken Language Systems: Research Directions for the Nineties

A spoken language system combines speech recognition, natural language processing and human interface technology. It functions by recognizing the person's words, interpreting the sequence of words to obtain a meaning in terms of the application, and providing an appropriate response back to the user. Potential applications of spoken language systems range from simple tasks, such as retrieving information from an existing database (traffic reports, airline schedules), to interactive problem solving tasks involving complex planning and reasoning (travel planning, traffic routing), to support for multilingual interactions. We examine eight key areas in which basic research is needed to produce spoken language systems: (1) robust speech recognition; (2) automatic training and adaptation; (3) spontaneous speech; (4) dialogue models; (5) natural language response generation; (6) speech synthesis and speech generation; (7) multilingual systems; and (8) interactive multimodal systems. In each area, we identify key research challenges, the infrastructure needed to support research, and the expected benefits. We conclude by reviewing the need for multidisciplinary research, for development of shared corpora and related resources, for computational support and far rapid communication among researchers. The successful development of this technology will increase accessibility of computers to a wide range of users, will facilitate multinational communication and trade, and will create new research specialties and jobs in this rapidly expanding area